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The television (TV) content ecosystem has shifted from traditional broadcasting systems to dedicated content producers and over-the-top (OTT) services. However, less empirical effort has been paid to the actual behaviors of the mobile users who watch TV content when explaining the impact of OTT service and mobile network profiles in watching TV content. This study aims to investigate the impact of gratifications and attitude formed by mobile TV users on actual mobile TV watching behaviors, as well as the moderating impacts of paid OTT service subscriptions and mobile network profiles, based on gratification theory, cognition–affect–behavioral (CAB) framework, sunk cost effect and walled-garden effect.

This study employs the generalized linear model (GLM) with generalized estimating equations (GEE) to test hypothesized relationships. A total of 338 mobile phone users who have been watching TV content using a mobile phone participated in the survey. The moderating variables, 4 types of paid streaming platform subscriptions, were classified based on the walled gardens formed by mobile telecom services.

The study’s results revealed that obtained gratifications and opportunity constructs substantially influenced a mobile phone user’s attitude and behaviors. Additionally, mobile network profiles and the degree of access to paid platform services played significant moderating roles in the relationship between users’ attitudes and behavior.

This research enriches the existing OTT service literature and is one of the pioneering studies investigating the walled-garden effect’s role in mobile phone users’ actual watching behaviors, offering valuable practical implications for the OTT platform providers.

To enhance the fit, comfort and overall satisfaction of lower body attire for online shoppers, this study introduces a reclassification method of the lower body profiles of young females in complex environments, which is used in the framework of remote clothing mass customization.

Frontal and lateral photographs were collected from 170 females prior, marked as size M. Employing a salient object detection algorithm suitable for complex backgrounds, precise segmentation of body profiles was achieved while refining the performance through transfer learning techniques. Subsequently, a skeletal detection algorithm was employed to delineate distinct human regions, from which 21 pivotal dimensional metrics were derived. These metrics underwent clustering procedures, thus establishing a systematic framework for categorizing the lower body shapes of young females. Building upon this foundation, a methodology for the body type combination across different body parts was proposed. This approach incorporated a frequency-based filtering mechanism to regulate the enumeration of body type combinations. The automated identification of body types was executed through a support vector machine (SVM) model, achieving an average accuracy exceeding 95% for each defined type.

Young females prior to being marked as the same lower garment size can be further subdivided based on their lower body types. Participants' torso types were classified into barrel-shaped, hip-convex and fat-accumulation types. Leg profile shapes were categorized into slender-elongated and short-stocky types. The frontal straightness of participants’ legs was classified as X-shaped, I-shaped and O-shaped types, while the leg side straightness was categorized based on the knee hyperextended degree. The number of combinations can be controlled based on the frequency of occurrence of combinations of different body types.

This methodological advancement serves as a robust cornerstone for optimizing clothing sizing and enabling remote clothing mass customization in E-commerce, providing assistance for body type database and clothing size database management as well as strategies for establishing a comprehensive remote customization supply chain and on-demand production model.